DataPart1
Clementine
20/09/2018
#Code for CSD in census
ct <- "35535"
cm <- "24462"
cv <- "59933"
#Code for central municipality in census
cmt <- "3520005"
cmv <- "5915022"
cmm <- "2466023"
#Coordinates of city hall
cht <- c(-79.5257363, 43.6148863)
chv <- c(-123.2309412, 49.2219987)
chm <- c(-73.7171664, 45.4726094)
#Critical distance for AirBnb
bdtv <- 10000
bdm <- 12000
#Files from inside airbnb
f <- list.files('insideairbnb/')
## Identifying the vectors for visible Minority status
parent_vector <- "v_CA16_3954"
minorities <- list_census_vectors("CA16") %>%
filter(vector == "v_CA16_3954") %>%
child_census_vectors(leaves_only = TRUE) %>%
pull(vector)
minority_vectors <- c(parent_vector, minorities)
lab <- as.data.frame (list_census_vectors('CA16') %>%
filter(vector %in% minorities) %>%
select(vector, label))montrealAirbnb <- read.csv(paste0('insideairbnb/', f[1]))
torontoAirbnb <- read.csv(paste0('insideairbnb/', f[2]))
vancouverAirbnb <- read.csv(paste0('insideairbnb/', f[3]))
df <- rbind(montrealAirbnb, torontoAirbnb, vancouverAirbnb)l <- levels(df$property_type)
lookup <- data.frame('type' = 1:length(l))
lookup$type <- as.factor(l)
lookup$property_group <- c(
# [1] "Aparthotel" "Apartment" "Bed and breakfast" "Boat" "Boutique hotel" "Bungalow" "Cabin"
'hotel', 'home', 'hotel', 'other', 'hotel', 'home', 'other',
# [8] "Camper/RV" "Campsite" "Casa particular (Cuba)" "Cave" "Chalet" "Condominium" "Cottage"
'other', 'other', 'home', 'other', 'home', 'home', 'home',
# [15] "Farm stay" "Guest suite" "Guesthouse" "Hostel" "Hotel" "House" "Houseboat"
'home', 'home', 'hotel', 'hotel', 'hotel', 'home', 'home',
# [22] "Hut" "Loft" "Nature lodge" "Other" "Serviced apartment" "Tent" "Timeshare"
'other', 'home', 'other', 'other', 'hotel', 'other', 'other',
# [29] "Tiny house" "Townhouse" "Villa" "Barn" "Castle" "Dorm" "Earth house"
'home', 'home', 'home', 'home', 'home', 'hotel', 'other',
# [36] "In-law" "Parking Space" "Treehouse" "Resort"
'home', 'other', 'other', 'hotel'
)
df <- data.frame(df,lookup[match(df$property_type, lookup$type),] )
# dfh = subset(df, property_group == 'home' & as.character(df$host_neighbourhood) == as.character(df$neighbourhood) & df$room_type != "Shared room")
dfh <- subset(df, property_group == 'home' & as.character(df$host_neighbourhood) == as.character(df$neighbourhood))
dfh$property_group <- NULL
dfhu <- dfh[!duplicated(dfh$host_id),]
dim(dfh)[1] - dim(df)[1]## [1] -11639dim(dfhu)[1] - dim(dfh)[1]## [1] -7168dfhu$year <- as.numeric(substr(dfhu$last_review, 1, 4))
dfhun <- subset(dfhu, year >= 2017)
dim(dfhun)[1] - dim(dfhu)[1]## [1] -7499dfhun$numPrice <- as.numeric(gsub("[$]",'',dfhun$price))## Warning: NAs introduits lors de la conversion automatiquesummary(dfhun$room_type)## Entire home/apt Private room Shared room
## 12163 4592 150final <- subset(dfhun, room_type != "Shared room")
final$rooms <- ifelse(final$bedrooms == 0, 1, final$bedrooms)
final$priceperroom <- as.numeric(ifelse(final$room_type == T, final$numPrice, final$numPrice / final$rooms))
dim(final) / dim(df)## [1] 0.3877485 1.0306122Census data of all Canadians Metros
# Apply function to get EI for all CSD for all Canadian CMAS
cmas <- list_census_regions("CA16") %>% filter(level == "CMA") %>% select(region, name, pop)## Querying CensusMapper API for regions data...cma_ei <- purrr::map_df(cmas$region, .f = diversity_csd) %>%
left_join(cmas, by = c("CMA"="region")) %>%
select(`Region Name`, `CMA Name`=name, CMA, GeoUID, Population, `CMA Population` = pop, Ei)## Census data is currently stored temporarily.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## You may add this option, together with your API key, to your .Rprofile.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
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## You may add this option, together with your API key, to your .Rprofile.
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## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.
##
##
## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.
##
##
## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.
##
##
## Querying CensusMapper API...##
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Downloading: 380 B## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
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## coercing into character vector
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector############# compute segregation for all metros and plot comparison of index
cma_seg <- purrr::map_df(cmas$region, .f = segregation_csd) %>%
left_join(cmas, by = c("cma"="region")) %>%
select(`Region Name`=name.x, `CMA Name`=name.y, CMA = cma,Population = csdpop, `CMA Population` = pop, Ei = csdei, H) %>%
ungroup()## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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Downloading: 9.4 kB## Querying CensusMapper API...##
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Downloading: 180 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
Downloading: 5.7 kB
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Downloading: 5.7 kB## Querying CensusMapper API...##
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Downloading: 140 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
Downloading: 6.1 kB
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Downloading: 6.1 kB
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Downloading: 6.1 kB## Querying CensusMapper API...##
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Downloading: 88 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 6.4 kB
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Downloading: 6.4 kB
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Downloading: 6.4 kB## Querying CensusMapper API...##
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Downloading: 76 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 55 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 4 kB
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Downloading: 66 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.4 kB
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Downloading: 66 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.4 kB
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Downloading: 3.4 kB## Querying CensusMapper API...##
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Downloading: 330 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.1 kB
Downloading: 3.1 kB
Downloading: 3.1 kB
Downloading: 3.1 kB
Downloading: 3.1 kB
Downloading: 3.1 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 32 kB
Downloading: 32 kB
Downloading: 32 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 3.1 kB
Downloading: 3.1 kB
Downloading: 3.1 kB
Downloading: 3.1 kB
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Downloading: 3.1 kB## Querying CensusMapper API...##
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Downloading: 100 kB
Downloading: 100 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 2.8 kB
Downloading: 2.8 kB
Downloading: 2.8 kB
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Downloading: 2.8 kB## Querying CensusMapper API...##
Downloading: 16 kB
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Downloading: 51 kB
Downloading: 51 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 2.4 kB
Downloading: 2.4 kB
Downloading: 2.4 kB
Downloading: 2.4 kB
Downloading: 2.4 kB
Downloading: 2.4 kB## Querying CensusMapper API...##
Downloading: 16 kB
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Downloading: 36 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 2.2 kB
Downloading: 2.2 kB
Downloading: 2.2 kB
Downloading: 2.2 kB
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Downloading: 2.2 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 34 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.9 kB
Downloading: 1.9 kB
Downloading: 1.9 kB
Downloading: 1.9 kB
Downloading: 1.9 kB
Downloading: 1.9 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
Downloading: 16 kB
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Downloading: 16 kB
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Downloading: 32 kB
Downloading: 32 kB
Downloading: 32 kB
Downloading: 32 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB## Querying CensusMapper API...##
Downloading: 16 kB
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Downloading: 76 kB
Downloading: 76 kB
Downloading: 76 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB## Querying CensusMapper API...##
Downloading: 12 kB
Downloading: 12 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB## Querying CensusMapper API...##
Downloading: 12 kB
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Downloading: 38 kB
Downloading: 38 kB
Downloading: 38 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
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Downloading: 24 kB
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Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB
Downloading: 1.7 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 52 kB
Downloading: 52 kB
Downloading: 52 kB
Downloading: 52 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB## Querying CensusMapper API...##
Downloading: 16 kB
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Downloading: 84 kB
Downloading: 84 kB
Downloading: 84 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB
Downloading: 1.6 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
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Downloading: 41 kB
Downloading: 41 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.5 kB
Downloading: 1.5 kB
Downloading: 1.5 kB
Downloading: 1.5 kB
Downloading: 1.5 kB
Downloading: 1.5 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 37 kB
Downloading: 37 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
Downloading: 7.6 kB
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Downloading: 12 kB
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Downloading: 13 kB
Downloading: 13 kB
Downloading: 13 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB## Querying CensusMapper API...##
Downloading: 16 kB
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Downloading: 30 kB
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Downloading: 30 kB
Downloading: 30 kB
Downloading: 30 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB## Querying CensusMapper API...##
Downloading: 16 kB
Downloading: 16 kB
Downloading: 21 kB
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Downloading: 21 kB
Downloading: 21 kB
Downloading: 21 kB
Downloading: 21 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB
Downloading: 1.8 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 100 kB
Downloading: 100 kB
Downloading: 100 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
Downloading: 20 kB
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Downloading: 25 kB
Downloading: 25 kB
Downloading: 25 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
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Downloading: 74 kB
Downloading: 74 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
Downloading: 3.6 kB
Downloading: 20 kB
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Downloading: 27 kB
Downloading: 27 kB
Downloading: 27 kB
Downloading: 27 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.1 kB
Downloading: 1.1 kB
Downloading: 1.1 kB
Downloading: 1.1 kB
Downloading: 1.1 kB
Downloading: 1.1 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 37 kB
Downloading: 37 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB
Downloading: 1.4 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 20 kB
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Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB## Querying CensusMapper API...##
Downloading: 3.6 kB
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Downloading: 20 kB
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Downloading: 35 kB
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Downloading: 35 kB
Downloading: 35 kB
Downloading: 35 kB## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB
Downloading: 1.3 kB## Querying CensusMapper API...##
Downloading: 16 kB
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.7 kB
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.1 kB
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.1 kB
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.1 kB
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
Downloading: 1.1 kB
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
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## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
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## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector## Adding missing grouping variables: `CSD_UID`ggplot(bind_rows(cma_seg %>%
filter(Population > 100000) %>%
clean_names2 %>%
top_n(10, -H),
cma_seg %>%
filter(Population > 100000) %>%
clean_names2 %>%
top_n(10, H)),
aes(y = H, x = reorder(`Region Name`, H), size = Population)) +
#geom_bar(stat = "identity") +
geom_point(colour = "#7e008c") +
coord_flip() +
theme_ipsum() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dotted"),
legend.position = "none") +
labs(y = "Segregation entropy index", x = "",
#title = "The most and the least segregated large cities in Canada",
caption = "Segregation index of visible minorities\n@dshkol | Data: Statistics Canada, Census 2016")
ggplot(cma_seg%>% filter(Population > 100000) %>% clean_names2 %>%
mutate(big_cma = ifelse(`CMA Population` > 1000000, `CMA Name`,"Other")),
aes(y= H, x = Ei, size = Population^1.5, colour = big_cma)) +
#geom_label_repel(aes(label = `Region Name`)) +
geom_text_repel(aes(label = `Region Name`)) +
scale_size_continuous(guide = FALSE) +
scale_colour_ipsum("", guide = FALSE) +
theme_ipsum() +
theme(panel.grid = element_blank(),
axis.text = element_blank()) +
labs(x = "More diverse \u2192", y = "More segregated \u2192",
caption = "Entropy index based calculations of diversity and segregation\nof visible minority groups in cities with population over 100,000\n@dshkol | Data: Statistics Canada, Census 2016")
Start city specific analysis
city <- "toronto"
if(city == "toronto"){
citynames <- unique(torontoAirbnb$city)
censuscode <- ct
cityhall <- cht
censusCodeMuni <- cmt
breakDist <- bdtv
}
if(city == "vancouver"){
citynames <- unique(vancouverAirbnb$city)
censuscode <- cv
cityhall <- chv
censusCodeMuni <- cmv
breakDist <- bdtv
}
if(city == "montreal"){
citynames <- unique(montrealAirbnb$city)
censuscode <- cm
cityhall <- chm
censusCodeMuni <- cmm
breakDist <- bdm
}
city_data <- subset(final, city %in% citynames)cma.ct <- get_census("CA16", regions=list(CMA=censuscode),
vectors = minority_vectors, level = "CT",
labels = "short", geo_format = NA)## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.cma.csd <- get_census("CA16", regions=list(CMA=censuscode),
vectors = minority_vectors, level = "CSD",
labels = "short", geo_format = NA)## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.
##
##
## Reading vectors data from local cache.csd.geo <- get_census_geometry("CA16", regions=list(CSD=censuscode),
level = "CT", geo_format = "sf")## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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level = "CSD", geo_format = "sf")## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.
##
##
## Querying CensusMapper API...##
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##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading vectors data from local cache.## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Reading geo data from local cache.## Joining, by = "GeoUID"pal <- colorQuantile(
palette = 'Blues',
domain = city_data$priceperroom,
n = 10
)
pal2 <- colorQuantile(
palette = 'Reds',
domain = tractTable$Ei,
n = 10)
map <- leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
addPolygons(
data = csd.csd.geo,
color = 'black',
fill = F,
weight = 0.7,
opacity = 0.9
) %>% addPolygons(
data = tractTable,
color = ~ pal2(Ei),
fill = ~ pal2(Ei),
weight = 0.4
) %>%
#addCircleMarkers(
# data = city_data,
# radius = ~ sqrt(4 * rooms),
# lat = ~ latitude,
# fillColor = ~ pal(priceperroom),
# color = 'black',
# stroke = T,
# fillOpacity = 0.5,
# weight = 0.1,
# layerId = ~ id,
# lng = ~ longitude
# ) %>%
# addLegend(pal = pal, position = 'topleft', values = city_data$priceperroom)%>%
addLegend(pal = pal2, position = 'topleft', values = tractTable$Ei)## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Reds is 9
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## Returning the palette you asked for with that many colorsmaplong.ct <- get_census("CA16", regions=list(CSD=censusCodeMuni),
vectors = minority_vectors, level = "CT",
labels = "detailed", geo_format = "sf")## Census data is currently stored temporarily.
##
## In order to speed up performance, reduce API quota usage, and reduce unnecessary network calls, please set up a persistent cache directory by setting options(cancensus.cache_path = '<path to cancensus cache directory>')
##
## You may add this option, together with your API key, to your .Rprofile.## Querying CensusMapper API...##
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Downloading: 180 kBnames(long.ct)[14:27] <- c("Total","White","South Asian","Chinese","Black","Filipino",
"Latin American","Arab","SE Asian","West Asian","Korean",
"Japanese","Other","Multiple")
long.ct.tidy <- long.ct %>%
select(-White) %>%
tidyr::gather(Group, Count, `South Asian`:Multiple) %>%
mutate(Proportion = Count/Total)
#head(as.data.frame(long.ct.tidy))
ggplot(long.ct.tidy) + geom_sf(aes(fill = Proportion^(1/2), colour = Proportion^(1/2))) +
scale_fill_viridis_c(option = 3, guide = FALSE) +
scale_colour_viridis_c(option = 3, guide = FALSE) +
theme_void() +
coord_sf(datum = NA) +
facet_wrap(~Group, ncol = 4) +
labs(caption = "Visible minority groups by square-root proportion of Census Tract population\n@dshkol | Data: Statistics Canada, Census 2016")
pal <- colorQuantile(
palette = 'Blues',
domain = city_data$priceperroom,
n = 10
)
map <- leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
addPolygons(
data = csd.csd.geo,
color = 'black',
fill = F,
weight = 0.7,
opacity = 0.9
) %>% addPolygons(
data = csd.geo,
color = 'grey',
fill = F,
weight = 0.4
) %>%
addCircleMarkers(
data = city_data,
radius = ~ sqrt(4 * rooms),
lat = ~ latitude,
fillColor = ~ pal(priceperroom),
color = 'black',
stroke = T,
fillOpacity = 0.5,
weight = 0.1,
layerId = ~ id,
lng = ~ longitude
) %>%
addLegend(pal = pal, position = 'topleft', values = city_data$priceperroom)## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colorsmap#############aggregate airbnb into city tracts.
city_tr <- csd.geo[csd.geo$CSD_UID == censusCodeMuni,]
#city_tr$vacancy = (city_tr$Dwellings - city_tr$Households) / city_tr$Dwellings
#save(toronto_tr, vancouver_tr, file="cityTractsWithPopAndVacancy.RData")
rbnb_pts <- city_data
coordinates(rbnb_pts) <- ~longitude+latitude
city_tracts <- as_Spatial(city_tr)
rbnb_pts@proj4string <-CRS("+proj=longlat +datum=WGS84")
city_tracts@proj4string <-CRS("+proj=longlat +datum=WGS84")
plot(city_tracts, border = "grey")
points(rbnb_pts, col = "red", cex = 0.2, pch = 16)
#class(city_tracts)
#class(rbnb_pts)
PTS <- as(rbnb_pts, "sf")
POLY <- as(city_tracts, "sf")
idata <- st_intersection(PTS, POLY)## although coordinates are longitude/latitude, st_intersection assumes that they are planar## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries#head(idata)
idata$distribPrice <- cut2(idata$priceperroom, g = 10)
intervals <- levels(unique(idata$distribPrice))
rbnbPerTract <- as.data.frame(idata %>%
#group_by(CSD_UID) %>%
count(GeoUID, sort = TRUE) %>%
select(GeoUID, n))[,1:2]
rbnbDistributionPerTract <- as.data.frame(idata %>%
#group_by(CSD_UID) %>%
count(GeoUID, distribPrice) %>%
select(GeoUID, distribPrice, n))[,1:3]
tractDistrib <- dcast(rbnbDistributionPerTract, GeoUID ~ distribPrice)## Using n as value column: use value.var to override.tractDistrib[is.na(tractDistrib)] <- 0
tractDistrib <- tractDistrib[,c("GeoUID", intervals)]
colnames(tractDistrib)[2:11] <- paste0("G", 1:10)
lookupDistrib <- data.frame('name' = paste0("G", 1:10), 'interval' = intervals)
listingCounts <- as.data.frame(rbnbPerTract[,1:2])
city_tracts@data <- data.frame(city_tracts@data,
listingCounts[match(city_tracts@data$GeoUID,
listingCounts$GeoUID),])
city_tracts@data <- data.frame(city_tracts@data,
tractDistrib[match(city_tracts@data$GeoUID,
tractDistrib$GeoUID),])
city_tracts@data <- data.frame(city_tracts@data,
tractTable[match(city_tracts@data$GeoUID,
tractTable$GeoUID),])Mino <- as.data.frame(cma.ct[,c("GeoUID", minorities)])
city_tracts@data <- data.frame(city_tracts@data,
Mino[match(city_tracts@data$GeoUID,
Mino$GeoUID),])
city_tracts@data$ListingPerCapita <- city_tracts@data$n / city_tracts@data$Population
city_tracts@data <- city_tracts@data %>%
mutate_at(minorities, funs(Share =./ Population) )
#summary(city_tracts@data)
centroidsCity <- gCentroid(city_tracts,byid=TRUE)
#plot(centroidsCity, add = T, pch = 16, cex = 0.6)
listMinoShares <- paste0(minorities, "_Share")
EstimatorAirbnbPresence <- cbind(city_tracts@data[,c("GeoUID", "ListingPerCapita", "Ei",
listMinoShares)],
coordinates(centroidsCity))
#head(EstimatorAirbnbPresence)
DistCityHall <- distm(EstimatorAirbnbPresence[,c('x','y')],
cityhall, fun=distVincentyEllipsoid)
EstimatorAirbnbPresence <- cbind(EstimatorAirbnbPresence, DistCityHall)
hist(EstimatorAirbnbPresence$DistCityHall)
#head(EstimatorAirbnbPresence)
ggplot(EstimatorAirbnbPresence, aes(x = DistCityHall, y = ListingPerCapita)) +
geom_point() + geom_smooth() + scale_x_log10() + scale_y_log10() +
geom_vline(xintercept = breakDist, col = "orange", cex = 1)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 55 rows containing non-finite values (stat_smooth).## Warning: Removed 55 rows containing missing values (geom_point).
# mean value of listing per capita of tracts between 0 & 10 km:
central <- EstimatorAirbnbPresence %>%
filter(DistCityHall <= breakDist, !is.na(ListingPerCapita)) %>%
select(ListingPerCapita, DistCityHall, Ei, listMinoShares)
# regression listing per capita of tracts above 10 km:
peripheral <- EstimatorAirbnbPresence %>%
filter(DistCityHall > breakDist, !is.na(ListingPerCapita)) %>%
select(ListingPerCapita, DistCityHall, Ei, listMinoShares)for (sample in c("all", "central", "peripheral")){
if (sample == "all") df <- EstimatorAirbnbPresence
if (sample == "central") df <- central
if (sample == "peripheral") df <- peripheral
f <- paste0("scale(log(ListingPerCapita)) ~ scale(log(DistCityHall)) + ",
paste0("scale(",listMinoShares[-1], ")", collapse = " + "))
model <- lm(formula(f), data = df)
print(paste0("obs = ", dim(df)[1]))
print(summary(model))
coeffs <- as.data.frame(summary(model)$coefficients)
coeffs$label <- substr(rownames(coeffs), 7, 17)
res <- data.frame(coeffs, lab[match(coeffs$label, lab$vector),])
res[,c("label", "vector")] <- NULL
assign(paste0("Results_", sample), res)
}## [1] "obs = 572"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.57836 -0.49532 -0.01496 0.50824 2.31458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04615 0.03493 -1.321 0.187008
## scale(log(DistCityHall)) -0.06803 0.04695 -1.449 0.147932
## scale(v_CA16_3960_Share) -0.27618 0.05015 -5.508 5.82e-08 ***
## scale(v_CA16_3963_Share) -0.15508 0.04646 -3.338 0.000906 ***
## scale(v_CA16_3966_Share) -0.20503 0.05508 -3.722 0.000220 ***
## scale(v_CA16_3969_Share) -0.25682 0.04081 -6.292 6.81e-10 ***
## scale(v_CA16_3972_Share) -0.16055 0.05481 -2.929 0.003556 **
## scale(v_CA16_3975_Share) 0.02231 0.03990 0.559 0.576284
## scale(v_CA16_3978_Share) 0.10553 0.05917 1.784 0.075079 .
## scale(v_CA16_3981_Share) -0.17017 0.05054 -3.367 0.000817 ***
## scale(v_CA16_3984_Share) 0.07030 0.04905 1.433 0.152407
## scale(v_CA16_3987_Share) 0.17224 0.03906 4.409 1.27e-05 ***
## scale(v_CA16_3990_Share) 0.04040 0.04816 0.839 0.401908
## scale(v_CA16_3993_Share) 0.10269 0.04417 2.325 0.020470 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7883 on 502 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.3932, Adjusted R-squared: 0.3775
## F-statistic: 25.02 on 13 and 502 DF, p-value: < 2.2e-16
##
## [1] "obs = 129"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.49437 -0.47483 0.08667 0.42039 1.52203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01220 0.06309 -0.193 0.847000
## scale(log(DistCityHall)) 0.26612 0.07872 3.380 0.000992 ***
## scale(v_CA16_3960_Share) -0.06791 0.07964 -0.853 0.395603
## scale(v_CA16_3963_Share) 0.44595 0.07945 5.613 1.42e-07 ***
## scale(v_CA16_3966_Share) -0.28827 0.09331 -3.089 0.002521 **
## scale(v_CA16_3969_Share) -0.04498 0.08377 -0.537 0.592318
## scale(v_CA16_3972_Share) -0.11881 0.09007 -1.319 0.189809
## scale(v_CA16_3975_Share) 0.19086 0.07210 2.647 0.009270 **
## scale(v_CA16_3978_Share) -0.05635 0.09086 -0.620 0.536355
## scale(v_CA16_3981_Share) -0.15375 0.07385 -2.082 0.039604 *
## scale(v_CA16_3984_Share) -0.14064 0.07321 -1.921 0.057227 .
## scale(v_CA16_3987_Share) 0.09434 0.06984 1.351 0.179418
## scale(v_CA16_3990_Share) 0.22782 0.07653 2.977 0.003555 **
## scale(v_CA16_3993_Share) 0.04045 0.07889 0.513 0.609078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7137 on 114 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.542, Adjusted R-squared: 0.4897
## F-statistic: 10.38 on 13 and 114 DF, p-value: 3.748e-14
##
## [1] "obs = 388"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.58511 -0.45024 -0.01079 0.45360 2.05590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.375e-15 3.468e-02 0.000 1.000000
## scale(log(DistCityHall)) -5.058e-01 5.097e-02 -9.923 < 2e-16 ***
## scale(v_CA16_3960_Share) -1.474e-01 4.938e-02 -2.986 0.003016 **
## scale(v_CA16_3963_Share) -3.871e-02 4.451e-02 -0.870 0.385008
## scale(v_CA16_3966_Share) 3.147e-03 5.554e-02 0.057 0.954841
## scale(v_CA16_3969_Share) -1.982e-01 4.021e-02 -4.929 1.24e-06 ***
## scale(v_CA16_3972_Share) -2.886e-01 5.411e-02 -5.333 1.68e-07 ***
## scale(v_CA16_3975_Share) -1.927e-04 3.735e-02 -0.005 0.995887
## scale(v_CA16_3978_Share) -1.442e-02 5.010e-02 -0.288 0.773702
## scale(v_CA16_3981_Share) -1.848e-01 5.207e-02 -3.549 0.000437 ***
## scale(v_CA16_3984_Share) 1.205e-01 5.287e-02 2.280 0.023180 *
## scale(v_CA16_3987_Share) 1.467e-01 4.015e-02 3.653 0.000296 ***
## scale(v_CA16_3990_Share) -1.306e-02 5.381e-02 -0.243 0.808356
## scale(v_CA16_3993_Share) 1.256e-01 4.237e-02 2.964 0.003230 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6832 on 374 degrees of freedom
## Multiple R-squared: 0.549, Adjusted R-squared: 0.5333
## F-statistic: 35.02 on 13 and 374 DF, p-value: < 2.2e-16#str(Results_all)
reg_total <- cbind(Results_all,Results_central, Results_peripheral)
reg_total[,c("t.value", "Std..Error", "t.value.1", "Std..Error.1", "label.1.1", "t.value.2", "Std..Error.2", "label.1.2")] <- NULL
colnames(reg_total) <- c("est_all", "pval_all", "minority", "est_central", "pval_central",
"est_peripheral", "pval_peripheral")
reg_total$variables <- rownames(reg_total)
reg_total$var <- ifelse(is.na(reg_total$minority), as.character(reg_total$variables), as.character(reg_total$minority))pall <- ggplot(reg_total, aes(x = var)) +
geom_bar(aes(y = est_all, fill = ifelse(pval_all < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank())
pcent <- ggplot(reg_total, aes(x = var)) +
geom_bar(aes(y = est_central, fill = ifelse(pval_central < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank() )
pper <- ggplot(reg_total, aes(x = var)) +
geom_bar(aes(y = est_peripheral, fill = ifelse(pval_peripheral < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
#scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank())
grid.arrange(pall, pcent, pper)
EstimatorAirbnbPrice <- data.frame(idata, EstimatorAirbnbPresence[match(idata$GeoUID,
EstimatorAirbnbPresence$GeoUID),])
# head(EstimatorAirbnbPrice)
# hist(EstimatorAirbnbPrice$priceperroom)
EstimatorAirbnbPrice_all <- EstimatorAirbnbPrice %>%
filter(!is.na(priceperroom), priceperroom > 0)
# mean value of listing per capita of tracts between 0 & 10 km:
central_P <- EstimatorAirbnbPrice_all %>%
filter(DistCityHall <= breakDist) #%>% select(ListingPerCapita, DistCityHall, Ei, listMinoShares)
peripheral_P <- EstimatorAirbnbPrice_all %>%
filter(DistCityHall > breakDist) #%>% select(ListingPerCapita, DistCityHall, Ei, listMinoShares)
for (sample in c("all", "central", "peripheral")){
if (sample == "all") df <- EstimatorAirbnbPrice_all
if (sample == "central") df <- central_P
if (sample == "peripheral") df <- peripheral_P
f <- paste0("scale(priceperroom) ~ scale(log(DistCityHall)) + ",
paste0("scale(",listMinoShares[-1], ")", collapse = " + "))
model <- lm(formula(f), data = df)
print(paste0("obs = ", dim(df)[1]))
print(summary(model))
coeffs <- as.data.frame(summary(model)$coefficients)
coeffs$label <- substr(rownames(coeffs), 7, 17)
res <- data.frame(coeffs, lab[match(coeffs$label, lab$vector),])
res[,c("label", "vector")] <- NULL
assign(paste0("Results_", sample, "Price"), res)
}## [1] "obs = 6804"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1719 -0.6074 -0.1775 0.3757 10.4938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0001715 0.0116023 0.015 0.98821
## scale(log(DistCityHall)) -0.0700329 0.0147916 -4.735 2.24e-06 ***
## scale(v_CA16_3960_Share) -0.0040501 0.0144759 -0.280 0.77965
## scale(v_CA16_3963_Share) -0.0333501 0.0148742 -2.242 0.02498 *
## scale(v_CA16_3966_Share) -0.0952697 0.0155276 -6.136 8.97e-10 ***
## scale(v_CA16_3969_Share) -0.1388939 0.0135952 -10.216 < 2e-16 ***
## scale(v_CA16_3972_Share) -0.0653657 0.0153048 -4.271 1.97e-05 ***
## scale(v_CA16_3975_Share) 0.2004058 0.0139660 14.350 < 2e-16 ***
## scale(v_CA16_3978_Share) 0.0208043 0.0150666 1.381 0.16738
## scale(v_CA16_3981_Share) -0.0212479 0.0184134 -1.154 0.24857
## scale(v_CA16_3984_Share) -0.0121957 0.0181560 -0.672 0.50179
## scale(v_CA16_3987_Share) -0.0077983 0.0128619 -0.606 0.54433
## scale(v_CA16_3990_Share) -0.0413930 0.0139754 -2.962 0.00307 **
## scale(v_CA16_3993_Share) 0.0883160 0.0134153 6.583 4.95e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.957 on 6789 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.086, Adjusted R-squared: 0.08424
## F-statistic: 49.13 on 13 and 6789 DF, p-value: < 2.2e-16
##
## [1] "obs = 1903"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1633 -0.6043 -0.1694 0.3830 8.9383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0002889 0.0218172 0.013 0.9894
## scale(log(DistCityHall)) 0.1532740 0.0267821 5.723 1.21e-08 ***
## scale(v_CA16_3960_Share) -0.0194645 0.0324980 -0.599 0.5493
## scale(v_CA16_3963_Share) 0.1299477 0.0291080 4.464 8.51e-06 ***
## scale(v_CA16_3966_Share) -0.0548723 0.0290338 -1.890 0.0589 .
## scale(v_CA16_3969_Share) 0.1323365 0.0315733 4.191 2.90e-05 ***
## scale(v_CA16_3972_Share) -0.1266276 0.0286055 -4.427 1.01e-05 ***
## scale(v_CA16_3975_Share) 0.2297991 0.0305528 7.521 8.33e-14 ***
## scale(v_CA16_3978_Share) -0.0496651 0.0330595 -1.502 0.1332
## scale(v_CA16_3981_Share) -0.0321204 0.0303847 -1.057 0.2906
## scale(v_CA16_3984_Share) -0.0199639 0.0265449 -0.752 0.4521
## scale(v_CA16_3987_Share) 0.0210175 0.0236057 0.890 0.3734
## scale(v_CA16_3990_Share) 0.0074506 0.0303174 0.246 0.8059
## scale(v_CA16_3993_Share) 0.0301178 0.0289459 1.040 0.2982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9515 on 1888 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1009, Adjusted R-squared: 0.09472
## F-statistic: 16.3 on 13 and 1888 DF, p-value: < 2.2e-16
##
## [1] "obs = 4901"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0587 -0.5515 -0.1483 0.3433 9.5876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.233e-15 1.300e-02 0.000 1.000000
## scale(log(DistCityHall)) -3.679e-01 1.776e-02 -20.712 < 2e-16 ***
## scale(v_CA16_3960_Share) 6.006e-02 1.764e-02 3.406 0.000666 ***
## scale(v_CA16_3963_Share) -3.237e-02 1.615e-02 -2.005 0.045063 *
## scale(v_CA16_3966_Share) -5.543e-02 1.950e-02 -2.842 0.004496 **
## scale(v_CA16_3969_Share) -8.936e-02 1.590e-02 -5.621 2.01e-08 ***
## scale(v_CA16_3972_Share) -1.197e-01 1.807e-02 -6.626 3.83e-11 ***
## scale(v_CA16_3975_Share) 1.142e-01 1.557e-02 7.335 2.58e-13 ***
## scale(v_CA16_3978_Share) -8.000e-03 1.672e-02 -0.479 0.632273
## scale(v_CA16_3981_Share) 3.352e-02 2.131e-02 1.573 0.115729
## scale(v_CA16_3984_Share) -6.840e-04 2.110e-02 -0.032 0.974140
## scale(v_CA16_3987_Share) -4.271e-02 1.458e-02 -2.930 0.003402 **
## scale(v_CA16_3990_Share) -4.077e-03 1.994e-02 -0.204 0.838005
## scale(v_CA16_3993_Share) 6.171e-02 1.459e-02 4.230 2.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9101 on 4887 degrees of freedom
## Multiple R-squared: 0.174, Adjusted R-squared: 0.1718
## F-statistic: 79.17 on 13 and 4887 DF, p-value: < 2.2e-16reg_total2 <- cbind(Results_allPrice,Results_centralPrice, Results_peripheralPrice)
reg_total2[,c("t.value", "Std..Error", "t.value.1", "Std..Error.1", "label.1.1", "t.value.2", "Std..Error.2", "label.1.2")] <- NULL
colnames(reg_total2) <- c("est_all", "pval_all", "minority", "est_central", "pval_central",
"est_peripheral", "pval_peripheral")
reg_total2$variables <- rownames(reg_total2)
reg_total2$var <- ifelse(is.na(reg_total2$minority), as.character(reg_total2$variables),
as.character(reg_total2$minority))pall2 <- ggplot(reg_total2, aes(x = var)) +
geom_bar(aes(y = est_all, fill = ifelse(pval_all < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank())
pcent2 <- ggplot(reg_total2, aes(x = var)) +
geom_bar(aes(y = est_central, fill = ifelse(pval_central < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank() )
pper2 <- ggplot(reg_total2, aes(x = var)) +
geom_bar(aes(y = est_peripheral, fill = ifelse(pval_peripheral < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
#scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() +
theme(legend.title=element_blank())
grid.arrange(pall, pall2,
pcent, pcent2,
pper, pper2,
ncol = 2)
ReardonSegregationAirbnbPrice <- segIndex10(tabOfSpatialUnits = city_tracts@data, distributionColNames = paste0("G", 1:10))
EntropySegregationMinorities <- cma_seg[cma_seg$CSD_UID == censusCodeMuni,"H"]
ReardonSegregationAirbnbPrice## [1] 0.2637335EntropySegregationMinorities## # A tibble: 1 x 1
## H
## <dbl>
## 1 0.180Including Plots
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